In practical applications, many 3D models consist of a large number of connected components. And these multi-connected 3D models usually contain lots of repetitive structures in various transformations, as shown in Fig. Efficient compression methods for this kind of 3D models should be able to extract the redundancy existing in the repetitive structures.
The owner of the current invention also co-owns a PCT application entitled “Efficient Compression Scheme for Large 3D Engineering Models” by K. Cai, Y. JIN, and Z. Chen (WO2010149492), which teaches a compression method for 3D models that consist of many small to medium sized connected components, and that have geometric features which repeat in various positions, scales and orientations, the teachings of which are specifically incorporated herein by reference. This method discovers the structures repeating in various positions, orientations and scaling factors. Then the 3D model is organized into “pattern-instance” representation. A pattern is the representative geometry of the corresponding repetitive structure. The connected components belonging to a repetitive structure are called instances of the corresponding pattern and represented by their transformation, i.e. the positions, orientations and possible scaling factors, regarding to the pattern. The orientation of an instance is represented by 2 orthogonal axes represented by (x0, y0, z0) and (x1, y1, z1) in Cartesian coordinate system, or (alpha, beta, gamma) in spherical coordinate system.
The owner of the current invention also co-owns a PCT application entitled “Bitstream Syntax and Semantics of Repetitive Structure Discovery Based 3D Model Compression Algorithm” by K. Cai, W. Jiang, and J. Tian (PCT/CN2011/076991), which teaches a two modes for compressing instance transformation data.